Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery

Dadrasjavan, F.; Zarrinpanjeh, N.; Ameri, A.

doi:10.20944/preprints201907.0009.v1

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…Automatic crack detection of road pavement based on aerial UAV imagery [75] SVM, edge-based approach This work proposes a programmed technique for crack recognition out and about asphalt using procured recordings from the UAV stage.…”

Section: Cnnmentioning

confidence: 99%

Machine learning-based pavement crack detection, classification, and characterization: a review

Ashraf,

Sophian,

Shafie

et al. 2023

Bulletin EEI

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The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.

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Section: Cnnmentioning

confidence: 99%

Machine learning-based pavement crack detection, classification, and characterization: a review

Ashraf,

Sophian,

Shafie

et al. 2023

Bulletin EEI

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“…The timely and proper maintenance of forest roads is essential for sustainable serviceability of roads, and an effective method to detect road surface problems is Unmanned Aerial Vehicles (UAV) (Tan and Li 2019). UAV's are considered useful tools for acquiring reliable information about the surface of the forest road (Dadrasjavan et al 2019). Ruzgienė et al (2015) showed that the correctness of the digital surface model for roads generally depends on camera resolution, ight height, and accuracy of ground control points.…”

Section: )mentioning

confidence: 99%

Development of a Sustainable Maintenance Strategy for Forest Road Wearing Course in Different Climate Zones

Motlagh

Parsakhoo

Najafi

et al. 2022

Preprint

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In the recent decade, forest roads with a gravel surface course constructed in the different climates of Hyrcanian zone have worn out due to maintenance problems. This study was done to investigate the effect of traffic, maintenance budget, and climate on deterioration of forest roads and determine the best time and type of maintenance activities in the Mediterranean, sub-humid and semi arid climates. Unmanned aerial vehicle (UAV) was used to monitoring Unpaved Road Condition Index (UPCI), immediately after maintenance activities and each season for one year. Moreover, deterioration time of the wearing course was predicted using Monte Carlo time series analysis in MATLAB. Results showed that sub-humid climate presented lower UPCI (7.19) compared to the two other climates. UPCI values for Mediterranean and semi-arid climates were 7.81 and 8.82, respectively. In most cases, where roads were maintained by high-budget strategy, deterioration time was longer than other strategies, but cost-effectiveness (CE) value of low-budget strategy was more than other strategies in all traffic levels of Mediterranean climate and high-traffic roads in semi-arid climate. Low-budget maintenance activities include one culvert improvement per 6 km, light blading, 30 mm layer gravelling. In semi-arid climate, medium-budget maintenance strategy was more efficient in medium and low-traffic roads. In, sub-humid climates it was detected that CE values severely vary depending on the level of traffic. Medium, high and low budget maintenance strategies were respectively efficient in high, medium and low-traffic roads. High budget maintenance activities include one culvert improvement per 4 km, heavy blading and local compaction, 60 mm layer gravelling. Overall, it was concluded that monitoring UPCI over time and probability analysis using time series is useful for sustainable and long term management of forest roads.

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“…6a) were automatically subjected to a pre-processing and enhancement process (Sobel operator and Prewit), then they were segmented (edge detector, Canny filter, Gaussian filter) and classified (Support Vector Machine) to be able to extrapolate the information we need (Fig. 6b) (Grüen & Li, 1997;Serna & Marcotegui, 2014;Ameri, Dadrass Javan, & Zarrinpanjeh, 2019;Dadrasjavan, Zarrinpanjeh, & Ameri, 2019). In particular, the SVM classification was carried out in two phases:…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

Techniques of Geomatics and Soft Computing for the Monitoring of Unsafe Buildings

Bernardo

Bilotta

2021

Proceedings ARQUEOLÓGICA 2.0 - 9th International Congress &Amp; 3rd GEORES - GEOmatics and pREServation

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The proposed research activity is part of the conservation of cultural heritage and is based on the study and development of advanced techniques for monitoring, inspecting and mapping building cracks in order to obtain and constantly update the state of building safety through a GIS platform. The data collection (initial and periodic) is one of the most important phases of the monitoring process and allows us to obtain information about the integrity of the buildings, essential in order to plan future design and intervention choices. This operation can be carried out both through traditional tools (3D laser scanners, GNSS receivers, motorized total station) and innovative tools (such as remote sensing or UAVs). The goal of the research was the design and construction of an innovative automated system for monitoring and continuous data acquisition (big data). Furthermore, we have implemented algorithms dedicated to the management of the amount of georeferenced data (big data) acquired. We optimized their representation on GIS (Geographic Information System) platforms in order to obtain an “open and updatable” thematic cartography, and set up a sort of Cadastre of unsafe buildings in the village of Casalvecchio Siculo (possibly extended to other villages in the future). This is intended (in our application) as an updatable IT tool for archiving, viewing, querying and managing all the data that the municipality and the regions have on their own villages. In it, it will be possible to represent the elements inherent to the geometric characteristics of the buildings, their relevance, the state of the cracks, the interventions carried out in the most important historic buildings and the systems created, having databases available that allow quick selective searches by topics.

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Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery

Cited by 18 publications

References 14 publications

Machine learning-based pavement crack detection, classification, and characterization: a review

Machine learning-based pavement crack detection, classification, and characterization: a review

Development of a Sustainable Maintenance Strategy for Forest Road Wearing Course in Different Climate Zones

Techniques of Geomatics and Soft Computing for the Monitoring of Unsafe Buildings

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